Dataset Information

The value of resting-state functional magnetic resonance imaging for detecting epileptogenic zones in patients with focal epilepsy.

ABSTRACT:

Objective

To determine the value of resting-state functional magnetic resonance imaging (RS-fMRI) based on the local analysis methods regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF), for detecting epileptogenic zones (EZs).

Methods

A total of 42 consecutive patients with focal epilepsy were enrolled. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of visually assessed RS-fMRI, MRI, magnetic resonance spectroscopy (MRS), video electroencephalography (VEEG), and positron-emission tomography computed tomography (PET-CT) in EZ localization were evaluated to assess their diagnostic abilities. ReHo, ALFF, and fALFF were also compared for their diagnostic values.

Results

RS-fMRI showed comparable sensitivity to PET (83.3%) and specificity to VEEG (66.7%), respectively, for EZ localization in patients with focal epilepsy. There were no significant differences between RS-fMRI and the other localization techniques in terms of sensitivity, specificity, PPV, and NPV. The sensitivities of ReHo, ALFF, and fALFF were 69.4%, 52.8%, and 38.9%, respectively, and for specificities of 66.7%, 83.3%, and 66.7%, respectively. There were no significant differences among ReHo, ALFF, and fALFF, except that ReHo was more sensitive than fALFF.

Conclusions

RS-fMRI may be an efficient tool for detecting EZs in focal epilepsy patients.

SUBMITTER: Chen Z

PROVIDER: S-EPMC5310782 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

The value of resting-state functional magnetic resonance imaging for detecting epileptogenic zones in patients with focal epilepsy.

Chen Zhijuan Z An Yang Y Zhao Bofeng B Yang Weidong W Yu Qing Q Cai Li L Ni Hongyan H Yin Jianzhong J

PloS one 20170215 2

<h4>Objective</h4>To determine the value of resting-state functional magnetic resonance imaging (RS-fMRI) based on the local analysis methods regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF), for detecting epileptogenic zones (EZs).<h4>Methods</h4>A total of 42 consecutive patients with focal epilepsy were enrolled. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of visually assessed RS-fMRI, M ...[more]

PMID: 28199371

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Project description:The purpose of this study was to prospectively investigate the agreement between the epileptogenic zone(s) (EZ) localization by resting-state functional magnetic resonance imaging (rs-fMRI) and the seizure onset zone(s) (SOZ) identified by intracranial electroencephalogram (ic-EEG) using novel differentiating and ranking criteria of rs-fMRI abnormal independent components (ICs) in a large consecutive heterogeneous pediatric intractable epilepsy population without an a priori alternate modality informing EZ localization or prior declaration of total SOZ number. The EZ determination criteria were developed by using independent component analysis (ICA) on rs-fMRI in an initial cohort of 350 pediatric patients evaluated for epilepsy surgery over a 3-year period. Subsequently, these rs-fMRI EZ criteria were applied prospectively to an evaluation cohort of 40 patients who underwent ic-EEG for SOZ identification. Thirty-seven of these patients had surgical resection/disconnection of the area believed to be the primary source of seizures. One-year seizure frequency rate was collected postoperatively. Among the total 40 patients evaluated, agreement between rs-fMRI EZ and ic-EEG SOZ was 90% (36/40; 95% confidence interval [CI], 0.76-0.97). Of the 37 patients who had surgical destruction of the area believed to be the primary source of seizures, 27 (73%) rs-fMRI EZ could be classified as true positives, 7 (18%) false positives, and 2 (5%) false negatives. Sensitivity of rs-fMRI EZ was 93% (95% CI 78-98%) with a positive predictive value of 79% (95% CI, 63-89%). In those with cryptogenic localization-related epilepsy, agreement between rs-fMRI EZ and ic-EEG SOZ was 89% (8/9; 95% CI, 0.52-99), with no statistically significant difference between the agreement in the cryptogenic and symptomatic localization-related epilepsy subgroups. Two children with negative ic-EEG had removal of the rs-fMRI EZ and were seizure free 1 year postoperatively. Of the 33 patients where at least 1 rs-fMRI EZ agreed with the ic-EEG SOZ, 24% had at least 1 additional rs-fMRI EZ outside the resection area. Of these patients with un-resected rs-fMRI EZ, 75% continued to have seizures 1 year later. Conversely, among 75% of patients in whom rs-fMRI agreed with ic-EEG SOZ and had no anatomically separate rs-fMRI EZ, only 24% continued to have seizures 1 year later. This relationship between extraneous rs-fMRI EZ and seizure outcome was statistically significant (p = 0.01). rs-fMRI EZ surgical destruction showed significant association with postoperative seizure outcome. The pediatric population with intractable epilepsy studied prospectively provides evidence for use of resting-state ICA ranking criteria, to identify rs-fMRI EZ, as developed by the lead author (V.L.B.). This is a high yield test in this population, because no seizure nor particular interictal epilepiform activity needs to occur during the study. Thus, rs-fMRI EZ detected by this technique are potentially informative for epilepsy surgery evaluation and planning in this population. Independent of other brain function testing modalities, such as simultaneous EEG-fMRI or electrical source imaging, contextual ranking of abnormal ICs of rs-fMRI localized EZs correlated with the gold standard of SOZ localization, ic-EEG, across the broad range of pediatric epilepsy surgery candidates, including those with cryptogenic epilepsy.

Project description:Alterations in the connectivity patterns of the fMRI-based resting-state networks (RSNs) have been reported in several types of epilepsies. Evidence pointed out these alterations might be associated with the genesis and propagation of interictal epileptiform discharges (IEDs). IEDs also evoke blood-oxygen-level dependent (BOLD) responses, which have been used to delineate irritative zones during preoperative work-up. Therefore, one may expect a relationship between the topology of the IED-evoked BOLD response network and the altered spatial patterns of the RSNs. In this study, we used EEG recordings and fMRI data obtained simultaneously from a chronic model of focal epilepsy in Wistar rats to verify our hypothesis. We found that IED-evoked BOLD response networks comprise both cortical and subcortical structures with a rat-dependent topology. In all rats, IEDs evoke both activation and deactivation types of BOLD responses. Using a Granger causality method, we found that in many cases areas with BOLD deactivation have directed influences on areas with activation (p<0.05). We were able to predict topological properties (i.e., focal/diffused, unilateral/bilateral) of the IED-evoked BOLD response network by performing hierarchical clustering analysis on major spatial features of the RSNs. All these results suggest that IEDs and disruptions in the RSNs found previously in humans may be different manifestations of the same transient events, probably reflecting altered consciousness. In our opinion, the shutdown of specific nodes of the default mode network may cause uncontrollable excitability in other functionally connected brain areas. We conclude that IED-evoked BOLD responses (i.e., activation and deactivation) and alterations of RSNs are intrinsically related, and speculate that an understanding of their interplay is necessary to discriminate focal epileptogenesis and network propagation phenomena across different brain modules via hub-based connectivity.

Project description:ObjectiveLocalization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for noninvasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional magnetic resonance imaging (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients.MethodsA total of 2132 healthy controls and 32 preoperative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data were used to generate training samples for a three-dimensional convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions.ResultsThe 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, and the resting state networks that colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with postsurgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel Class >I compared to Engel Class I.SignificanceNoninvasive techniques capable of localizing the seizure onset zone could improve presurgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning.

Project description:Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Dataset Information

The value of resting-state functional magnetic resonance imaging for detecting epileptogenic zones in patients with focal epilepsy.

Objective

Methods

Results

Conclusions

Publications

The value of resting-state functional magnetic resonance imaging for detecting epileptogenic zones in patients with focal epilepsy.

Similar Datasets

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